Interdisciplinary integration teaching for enhancing awareness and practice of ultrasound medical diagnosis based on artificial intelligence

doi:10. 3969/j. issn. 2096-045X. 2026. 02. 001

Abstract

Abstract:

Objective　To design an interdisciplinary teaching curriculum focused on artificial intelligence (AI) literacy for ultrasound diagnosis, and explore effective pathways for multidisciplinary integrated training, so as to enhance the application awareness and practical skills of residents in the department of ultrasound regarding AI-assisted ultrasound diagnosis, and address the issue of low clinical translation rate of AI research achievements.Methods　A total of 48 residents who received training in the Ultrasound Department of Beijing Tiantan Hospital, Capital Medical University from January 2023 to November 2025 were selected as the research subjects. A questionnaire survey was conducted to understand the current status of residents' awareness of AI-assisted ultrasound diagnosis. An interdisciplinary teaching program was designed incorporating clinical literature seminars, simulated operation of AI-assisted software, and clinical teaching sessions. Another questionnaire was administered to evaluate the feasibility and acceptance of this teaching program.Results　Residents demonstrated a moderate level of understanding of AI-assisted ultrasound diagnosis, with a lack of relevant training and practical experience. 89.6% (43/48) of residents expressed a desire to receive AI literacy education. 97.9% (47/48) of residents gave positive feedback on the interdisciplinary education course for AI-assisted ultrasound diagnosis, noting that it helped them construct a multidisciplinary knowledge framework, cultivate innovative thinking, develop practical operational skills, and meet the demands of clinical work.Conclusion　Residents in the ultrasound department possess basic awareness of AI-assisted ultrasound diagnosis and have an urgent learning need. The interdisciplinary literacy education for AI-assisted ultrasound diagnosis has been widely recognized by residents, and can serve as a new clinical teaching approach for AI literacy cultivation, fostering interdisciplinary medical talents and promoting the innovation of intelligent healthcare.

Key words: , artificial intelligence｜ medical teaching｜interdisciplinary integration｜ ultrasound diagnosis｜ resident

CLC Number:

R445

Gan Yuyang, Zheng Jialin, Chen Tao, Song Haiman, Yu Tengfei. Interdisciplinary integration teaching for enhancing awareness and practice of ultrasound medical diagnosis based on artificial intelligence[J]. Medical Education Management, 2026, 12(2): 133-141.

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https://journal06.magtechjournal.com/Jwk3_yyyzlt/EN/Y2026/V12/I2/133

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